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Reasoning Language Models for complex assessments tasks: Evaluating parental cooperation from child protection case reports

Dragan Stoll, Brian E. Perron, Zia Qi, Selina Steinmann, Nicole F. Eicher, Andreas Jud · Feb 15, 2026 · Citations: 0

Automatic Metrics
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks. We examined their potential to assess parental cooperation during CPS interventions using case reports, a case factor characterized by ambiguous and conflicting information. Methods: A four stage workflow comprising (1) case reports collection, (2) reasoning-based assessment of parental cooperation, (3) automated category extraction, and (4) case labeling was developed. The performance of RLMs with different parameter sizes (255B, 32B, 4B) was compared against human validated data. Two expert human reviewers (EHRs) independently classified a weighted random sample of reports. Results: The largest RLM achieved the highest accuracy (89%), outperforming the initial approach (80%). Classification accuracy was higher for mothers (93%) than for fathers (85%), and EHRs exhibited similar differences. Conclusions: RLMs' reasoning can effectively assess complex case factors such as parental cooperation. Lower accuracy in assessing fathers' cooperation supports the argument of a stronger professional focus on mothers in CPS interventions.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks."

Reported Metrics

partial

Accuracy

Useful for evaluation criteria comparison.

"Results: The largest RLM achieved the highest accuracy (89%), outperforming the initial approach (80%)."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Two expert human reviewers (EHRs) independently classified a weighted random sample of reports."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

accuracy

Research Brief

Metadata summary

Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks.
  • We examined their potential to assess parental cooperation during CPS interventions using case reports, a case factor characterized by ambiguous and conflicting information.
  • Methods: A four stage workflow comprising (1) case reports collection, (2) reasoning-based assessment of parental cooperation, (3) automated category extraction, and (4) case labeling was developed.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • The performance of RLMs with different parameter sizes (255B, 32B, 4B) was compared against human validated data.
  • Two expert human reviewers (EHRs) independently classified a weighted random sample of reports.
  • Results: The largest RLM achieved the highest accuracy (89%), outperforming the initial approach (80%).

Why It Matters For Eval

  • The performance of RLMs with different parameter sizes (255B, 32B, 4B) was compared against human validated data.
  • Two expert human reviewers (EHRs) independently classified a weighted random sample of reports.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: accuracy

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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